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README.md
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# Topic Drift Detector Model
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## Version: v20241225_085248
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This model detects topic drift in conversations using an enhanced attention-based architecture.
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## Model Architecture
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- Multi-head attention mechanism
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- Bidirectional LSTM for pattern detection
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- Dynamic weight generation
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- Semantic bridge detection
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## Performance Metrics
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```txt
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=== Full Training Results ===
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Best Validation RMSE: 0.0107
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Best Validation R²: 0.8867
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=== Test Set Results ===
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Loss: 0.0002
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RMSE: 0.0129
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R²: 0.8373
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```
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## Training Curves
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
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## Usage
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```python
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import torch
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# Load model
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model = torch.load('models/v20241225_085248/topic_drift_model.pt')
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# Use model for inference
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# Input shape: [batch_size, sequence_length * embedding_dim]
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# Output shape: [batch_size, 1] (drift score between 0 and 1)
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```
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